Track voluntary engagement measures alongside learning gains and study environments where bypass is possible¶

Strategy · All strategies
Evidence · 2 claims (2 for) · 2 studies (2 theoretical), q1–q2 · 0 of 2 report an effect size · 2 claims rest on one study

Description¶

The paper recommends that researchers consistently track and value voluntary engagement measures alongside learning gains, and study environments where bypass is present, since studies eliminating the possibility of bypass carry higher cost in ecological validity. It calls for longitudinal work on sustained AI use, mixed-methods approaches capturing why students engage or disengage, and deeper interdisciplinary partnerships with motivation scientists.

Design Implications¶

Context¶

Requirements¶

• AI literacy measurement is needed to interpret behavioral data, since a student heavily using ChatGPT may be exhibiting productive tool use or an abdication of thinking, which logs alone cannot distinguish

Constraints¶

• The paper warns that process-based assessment impact is blunted when institutions continue to credential only outputs

Target Learners¶

• AIED researchers and practitioners evaluating AI-based learning systems

Target Learning Goals¶

• Authentic engagement and self-regulatory capacity in environments where AI bypass is available

Claims¶

• AI assistance reduces persistence: persistence costs concentrate among learners who used AI for direct solutions, not hints [+W]
• Students' generative AI use may function either as a scaffold supporting thinking or as a shortcut bypassing essential cognitive processes [+W]

Related Strategies¶

• Track disaggregated dual enrollment pass rates in accountability frameworks alongside enrollment rates

Examples¶

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Key Sources¶

• H. Chad Lane. (2026). AIED's Unfinished Mission: Centering Agency and Motivation in the Age of Effortless Bypass. AIED 2026, CCIS 3032, pp. 502–516. https://doi.org/10.1007/978-3-032-29791-4_38